Antialiasing is a technique used to reduce or eliminate the appearance of undesired artifacts in an image. Typically, antialiasing techniques are applied to rasterized image data to reduce the visibility of jagged edges along boundaries and to soften abrupt transitions between different gray levels.
Applying an antialiasing process to an image that contains multiple regions representing different modes (such as text, graphics or photographic images) presents special difficulties. It is often preferable to apply different antialiasing algorithms to different groups of pixels depending on their role within the image. For example, it is often desirable to preserve and even enhance the distinct edges of text characters. It may also be desirable to maintain a clear boundary around a graphical image that is superimposed on a photographic image. In contrast, it may be preferable to apply a smoothing algorithm to an edge or boundary within a region. Performing an antialiasing operation without distinguishing between the different modes within an image may result in a reduction in image quality.
A common technique employed to distinguish between regions assigns a “label” to a pixel in an image indicating whether the pixel is used to render text, graphics, photographs, or another mode. Many known antialiasing techniques are designed to interpret these labels and selectively to apply a different antialiasing algorithm to each type of pixel, or leave some pixels unaltered. Such techniques tend to be efficient and produce high-quality output images.
One such technique is described in U.S. patent application Ser. No. 10/047,326, filed Jan. 14, 2002, which is incorporated herein by reference in its entirety. This technique enhances the appearance of edges in text and graphics regions by identifying a “boundary pixel” located on a boundary of a text or graphics object, tracing a group of boundary pixels to construct a local boundary segment, parameterizing and smoothing the local boundary segment to generate a new local boundary segment, and using the new local boundary segment to render the object. In one implementation of this technique, instructions for parameterizing and smoothing various boundary segments are pre-computed, stored in a look-up table, and directly accessed during the parameterizing and smoothing of local boundary segments.
Because techniques such as the one described above are designed to be used with labeled data, they cannot be used effectively with non-labeled data such as image data processed by a copier device or non-labeled data presented to a printing device. If an antialiasing technique designed for labeled image data is applied to non-labeled image data, the antialiasing processor will fail to distinguish between text and graphics pixels and smooth all boundaries in an image although it may be desirable to preserve the sharpness of some boundary pixels. There is a need to develop a technique allowing for antialiasing of non-labeled image data while preserving some of the boundary information present in the image data.
An additional challenge that arises in antialiasing relates to the rendition of pixels by pulse width modulation techniques. To apply pulse-width modulation, an output device divides the area represented by a pixel, referred to as the pixel cell, into a number of sub-elements and renders only a specified number of these sub-elements. For example, in the case of a printing device, toner particles may be deposited within several sub-elements on the left side of a pixel cell. A portion of a pixel cell rendered in this manner is referred to as a subpixel. A subpixel may cover an entire pixel cell.
When an antialiasing algorithm designed to smooth boundaries is applied to pixels that are on or near a boundary in an image, the antialiasing process typically “smears” across a region of pixels the gray level values that were originally concentrated along the boundary. If the antialiased pixels are rendered using conventional pulse width modulation techniques that do not account for the original boundary location, the subpixels may be placed in a dispersed pattern that causes the boundary information to be lost. There is a need to develop a method of determining an optimal location for a subpixel such that the boundary information present in the image data is preserved.